[Mlir-commits] [mlir] [mlir][linalg] Implement Conv2D using Winograd Conv2D algorithm (PR #96181)

[Oleksandr Alex Zinenko]

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@@ -0,0 +1,248 @@
+// RUN: mlir-opt %s -split-input-file -test-linalg-transform-patterns=test-winograd-conv2d | FileCheck %s
+
+func.func @conv2d_4x4_3x3(%arg0: tensor<2x6x6x5xf32>, %arg1: tensor<2x3x3x5xf32>, %arg2: tensor<1xf32>) -> tensor<2x4x4x2xf32> {
+  %0 = tensor.empty() : tensor<2x4x4x2xf32>
+  %1 = linalg.generic {indexing_maps = [affine_map<(d0, d1, d2, d3) -> (0)>, affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg2 : tensor<1xf32>) outs(%0 : tensor<2x4x4x2xf32>) {
+  ^bb0(%in: f32, %out: f32):
+    linalg.yield %in : f32
+  } -> tensor<2x4x4x2xf32>
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ftynse wrote:

Why do we have this in the test?

https://github.com/llvm/llvm-project/pull/96181